System and method for increasing data transmission rates through a content distribution network

ABSTRACT

Systems and methods for increasing data transmission rates through a content distribution network by generating a customized aggregation comprising data packets selected to maximize a data acceptance rate are disclosed herein. The system can include a memory having a content library database storing a plurality of data packets and a user profile database. The system can further include a server that can receive aggregation information identifying a plurality of delivery data packets and a plurality of assessment data packets, receive data packet data from the content library database, and generate an updated aggregation by removing at least one data packet from the aggregation.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/072,910, filed on Oct. 30, 2014, the entirety of which is herebyincorporated by reference herein.

BACKGROUND

This application relates to the field data transmission and networkoptimization.

A computer network or data network is a telecommunications network whichallows computers to exchange data. In computer networks, networkedcomputing devices exchange data with each other along network links(data connections). The connections between nodes are established usingeither cable media or wireless media. The best-known computer network isthe Internet.

Network computer devices that originate, route and terminate the dataare called network nodes. Nodes can include hosts such as personalcomputers, phones, servers as well as networking hardware. Two suchdevices can be said to be networked together when one device is able toexchange information with the other device, whether or not they have adirect connection to each other.

Computer networks differ in the transmission media used to carry theirsignals, the communications protocols to organize network traffic, thenetwork's size, topology and organizational intent. In most cases,communications protocols are layered on (i.e. work using) other morespecific or more general communications protocols, except for thephysical layer that directly deals with the transmission media.

As the volume of data exchanged between nodes in computer networks hasincreased, the speed of data transmission has become increasingly moreimportant. Although current technologies provide improved speeds ascompared to their predecessors, further developments are needed.

BRIEF SUMMARY

One aspect of the present disclosure relates to a system for increasingdata transmission rates through a content distribution network bygenerating a customized aggregation comprising data packets selected tomaximize a data acceptance rate. The system includes a memory including:a content library database comprising a plurality of data packets, whichplurality of data packets include a plurality of delivery data packetsand a plurality of assessment data packets; and a user profile database,which user profile database includes information identifying a cohort ofusers, and which user profile database includes information identifyinga plurality of at least one attribute of each of the users in the cohortof users. The system includes a server that can: receive aggregationinformation identifying a plurality of delivery data packets and aplurality of assessment data packets; receive data packet data from thecontent library database; identify a recipient cohort, which recipientcohort includes the group of users designated to receive the aggregationvia a plurality of user devices; generate a plurality of sub-cohorts bydividing the cohort into smaller groups, which users in each of thesub-cohorts share a common attribute; generate combined aggregation datacharacterizing the aggregation as a whole; generate an updatedaggregation by removing at least one data packet from the aggregation;and provide the updated aggregation to the users in the sub-cohort.

In some embodiments, the data packet data can include data packet userdata and data packet metadata. In some embodiments, the system includesa plurality of user devices connected to the server via a communicationnetwork. In some embodiments, the server can generate sub-cohort data,which sub-cohort data can be generated for each of the sub-cohorts fromdata of users in that sub-cohort. In some embodiments, the combinedaggregation data identifies a data acceptance rate.

In some embodiments, the server can identify the at least one datapacket for removal by determining a difference between the aggregationdata to the sub-cohort data, and comparing the difference to athreshold. In some embodiments generating an updated aggregation furtherincludes adding at least one new data packet in the aggregation. In someembodiments, the at least one new data packet is selected for inclusionin the aggregation based on a degree of in minimizing the differencebetween the aggregation data and the sub-cohort data. In someembodiments, the at least one new data packet is selected for inclusionin the aggregation based on a degree of optimizing the differencebetween the aggregation data and the sub-cohort data. In someembodiments, the combined aggregation data identifies a difficulty.

One aspect of the present disclosure relates to a method for increasingdata transmission rates through a content distribution network bygenerating a customized aggregation comprising data packets selected tomaximize a data acceptance rate. The method includes receivingaggregation information identifying a plurality of delivery data packetsand a plurality of assessment data packets, which plurality of deliverydata packets and which plurality of assessment data packets are storedin a content library database; receiving with a server data packet datafrom the content library database, which data packet data identifies oneor several attributes of the delivery data packets and the assessmentdata packets in the aggregation information; identifying with the servera recipient cohort, which recipient cohort includes the group of usersdesignated to receive the aggregation via a plurality of user devices;and generating with the server a plurality of sub-cohorts by dividingthe cohort into smaller groups, which users in each of the sub-cohortsshare a common attribute. The method can include generating with theserver combined aggregation data characterizing the aggregation as awhole, generating with the server an updated aggregation by removing atleast one data packet from the aggregation, wherein the aggregation isupdated to increase a data acceptance rate, and providing with theserver the updated aggregation to the users in the sub-cohort via aplurality of user devices connected to the server by a communicationnetwork.

In some embodiments, the data packet data includes data packet user dataand data packet metadata. In some embodiments, the method includesgenerating sub-cohort data, which sub-cohort data can be generated foreach of the sub-cohorts from data of users in that sub-cohort. In someembodiments, the combined aggregation data identifies a data acceptancerate. In some embodiments, the method can include identifying the atleast one data packet for removal by determining a difference betweenthe aggregation data to the sub-cohort data, and comparing thedifference to a threshold.

In some embodiments, generating an updated aggregation further includesincluding at least one new data packet in the aggregation. In someembodiments, the at least one new data packet is selected for inclusionin the aggregation based on a degree of in minimizing the differencebetween the aggregation data and the sub-cohort data. In someembodiments, the at least one new data packet is selected for inclusionin the aggregation based on a degree of optimizing the differencebetween the aggregation data and the sub-cohort data. In someembodiments, the combined aggregation data identifies a difficulty.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples, whileindicating various embodiments, are intended for purposes ofillustration only and are not intended to necessarily limit the scope ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing illustrating an example of a contentdistribution network.

FIG. 2 is a block diagram illustrating a computer server and computingenvironment within a content distribution network.

FIG. 3 is a block diagram illustrating an embodiment of one or more datastore servers within a content distribution network.

FIG. 4 is a block diagram illustrating an embodiment of one or morecontent management servers within a content distribution network.

FIG. 5 is a block diagram illustrating the physical and logicalcomponents of a special-purpose computer device within a contentdistribution network.

FIG. 6 contains a plurality of images of data acceptance curves.

FIG. 7 contains a plurality of images of data packet curves.

FIG. 8 is a flowchart illustrating one embodiment of a process forgenerating a customized aggregation to maximize data transmission ratesthrough a content distribution network.

FIG. 9 is a flowchart illustrating one embodiment of a process forgenerating a customized aggregation matched to a sub-cohort to maximizedata transmission rates through the content distribution network.

FIG. 10 is a flowchart illustrating one embodiment of a process forgenerating an aggregation database.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and isnot intended to limit the scope, applicability or configuration of thedisclosure. Rather, the ensuing description of the illustrativeembodiment(s) will provide those skilled in the art with an enablingdescription for implementing a preferred exemplary embodiment. It isunderstood that various changes can be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

With reference now to FIG. 1, a block diagram is shown illustratingvarious components of a content distribution network (CDN) 100 whichimplements and supports certain embodiments and features describedherein. Content distribution network 100, also referred to herein as theprediction system 100, may include one or more content managementservers 102. As discussed below in more detail, content managementservers 102 may be any desired type of server including, for example, arack server, a tower server, a miniature server, a blade server, a minirack server, a mobile server, an ultra-dense server, a super server, orthe like, and may include various hardware components, for example, amotherboard, a processing units, memory systems, hard drives, networkinterfaces, power supplies, etc. Content management server 102 mayinclude one or more server farms, clusters, or any other appropriatearrangement and/or combination or computer servers. Content managementserver 102 may act according to stored instructions located in a memorysubsystem of the server 102, and may run an operating system, includingany commercially available server operating system and/or any otheroperating systems discussed herein.

The content distribution network 100 may include one or more data storeservers 104, such as database servers and file-based storage systems.The database servers 104 can access data that can be stored on a varietyof hardware components. These hardware components can include, forexample, components forming tier 0 storage, components forming tier 1storage, components forming tier 2 storage, and/or any other tier ofstorage. In some embodiments, tier 0 storage refers to storage that isthe fastest tier of storage in the database server 104, andparticularly, the tier 0 storage is the fastest storage that is not RAMor cache memory. In some embodiments, the tier 0 memory can be embodiedin solid state memory such as, for example, a solid-state drive (SSD)and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one orseveral higher performing systems in the memory management system, andthat is relatively slower than tier 0 memory, and relatively faster thanother tiers of memory. The tier 1 memory can be one or several harddisks that can be, for example, high-performance hard disks. These harddisks can be one or both of physically or communicatingly connected suchas, for example, by one or several fiber channels. In some embodiments,the one or several disks can be arranged into a disk storage system, andspecifically can be arranged into an enterprise class disk storagesystem. The disk storage system can include any desired level ofredundancy to protect data stored therein, and in one embodiment, thedisk storage system can be made with grid architecture that createsparallelism for uniform allocation of system resources and balanced datadistribution.

In some embodiments, the tier 2 storage refers to storage that includesone or several relatively lower performing systems in the memorymanagement system, as compared to the tier 1 and tier 2 storages. Thus,tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier2 memory can include one or several SATA-drives or one or severalNL-SATA drives.

In some embodiments, the one or several hardware and/or softwarecomponents of the database server 104 can be arranged into one orseveral storage area networks (SAN), which one or several storage areanetworks can be one or several dedicated networks that provide access todata storage, and particularly that provides access to consolidated,block level data storage. A SAN typically has its own network of storagedevices that are generally not accessible through the local area network(LAN) by other devices. The SAN allows access to these devices in amanner such that these devices appear to be locally attached to the userdevice.

Data stores 104 may comprise stored data relevant to the functions ofthe content distribution network 100. Illustrative examples of datastores 104 that may be maintained in certain embodiments of the contentdistribution network 100 are described below in reference to FIG. 3. Insome embodiments, multiple data stores may reside on a single server104, either using the same storage components of server 104 or usingdifferent physical storage components to assure data security andintegrity between data stores. In other embodiments, each data store mayhave a separate dedicated data store server 104.

Content distribution network 100 also may include one or more userdevices 106 and/or supervisor devices 110. User devices 106 andsupervisor devices 110 may display content received via the contentdistribution network 100, and may support various types of userinteractions with the content. User devices 106 and supervisor devices110 may include mobile devices such as smartphones, tablet computers,personal digital assistants, and wearable computing devices. Such mobiledevices may run a variety of mobile operating systems, and may beenabled for Internet, e-mail, short message service (SMS), Bluetooth®,mobile radio-frequency identification (M-RFID), and/or othercommunication protocols. Other user devices 106 and supervisor devices110 may be general purpose personal computers or special-purposecomputing devices including, by way of example, personal computers,laptop computers, workstation computers, projection devices, andinteractive room display systems. Additionally, user devices 106 andsupervisor devices 110 may be any other electronic devices, such asthin-client computers, Internet-enabled gaming systems, business or homeappliances, and/or personal messaging devices, capable of communicatingover network(s) 120.

In different contexts of content distribution networks 100, user devices106 and supervisor devices 110 may correspond to different types ofspecialized devices, for example, student devices and teacher devices inan educational network, employee devices and presentation devices in acompany network, different gaming devices in a gaming network, etc. Insome embodiments, user devices 106 and supervisor devices 110 mayoperate in the same physical location 107, such as a classroom orconference room. In such cases, the devices may contain components thatsupport direct communications with other nearby devices, such aswireless transceivers and wireless communications interfaces, Ethernetsockets or other Local Area Network (LAN) interfaces, etc. In otherimplementations, the user devices 106 and supervisor devices 110 neednot be used at the same location 107, but may be used in remotegeographic locations in which each user device 106 and supervisor device110 may use security features and/or specialized hardware (e.g.,hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.) tocommunicate with the content management server 102 and/or other remotelylocated user devices 106. Additionally, different user devices 106 andsupervisor devices 110 may be assigned different designated roles, suchas presenter devices, teacher devices, administrator devices, or thelike, and in such cases the different devices may be provided withadditional hardware and/or software components to provide content andsupport user capabilities not available to the other devices.

In some embodiments, the content distribution network 100 can include alarge number of user devices 106 such as, for example, 100, 500, 1,000,2,000, 4,000, 6,000, 8,000, 10,000, 50,000, 100,000, 250,000, 1,000,000,5,000,000, 10,000,000, 100,000,000, 500,000,000 and/or any other orintermediate number of user devices 106. In some embodiments, the largenumber of user devices 106 can enable the functioning of the contentdistribution network 100. Specifically, the large number of user devices106 can allow a large number of students to interact with the contentdistribution network 100 to thereby generate the data volume to enableperforming of the methods and processes discussed at length below. Insome embodiments, this volume of data can be so large that it cannot beprocessed by a human. Such a volume of data is referred to herein as amassive data volume.

The content distribution network 100 also may include a privacy server108 that maintains private user information at the privacy server 108while using applications or services hosted on other servers. Forexample, the privacy server 108 may be used to maintain private data ofa user within one jurisdiction even though the user is accessing anapplication hosted on a server (e.g., the content management server 102)located outside the jurisdiction. In such cases, the privacy server 108may intercept communications between a user device 106 or supervisordevice 110 and other devices that include private user information. Theprivacy server 108 may create a token or identifier that does notdisclose the private information and may use the token or identifierwhen communicating with the other servers and systems, instead of usingthe user's private information.

As illustrated in FIG. 1, the content management server 102 may be incommunication with one or more additional servers, such as a contentserver 112, a user data server 114, and/or an administrator server 116.Each of these servers may include some or all of the same physical andlogical components as the content management server(s) 102, and in somecases, the hardware and software components of these servers 112-116 maybe incorporated into the content management server(s) 102, rather thanbeing implemented as separate computer servers.

Content server 112 may include hardware and software components togenerate, store, and maintain the content resources for distribution touser devices 106 and other devices in the network 100. For example, incontent distribution networks 100 used for professional training andeducational purposes, content server 112 may include data stores oftraining materials, presentations, plans, syllabi, reviews, evaluations,interactive programs and simulations, course models, course outlines,and various training interfaces that correspond to different materialsand/or different types of user devices 106. In content distributionnetworks 100 used for media distribution, interactive gaming, and thelike, a content server 112 may include media content files such asmusic, movies, television programming, games, and advertisements.

User data server 114 may include hardware and software components thatstore and process data for multiple users relating to each user'sactivities and usage of the content distribution network 100. Forexample, the content management server 102 may record and track eachuser's system usage, including their user device 106, content resourcesaccessed, and interactions with other user devices 106. This data may bestored and processed by the user data server 114, to support usertracking and analysis features. For instance, in the professionaltraining and educational contexts, the user data server 114 may storeand analyze each user's training materials viewed, presentationsattended, courses completed, interactions, evaluation results, and thelike. The user data server 114 may also include a repository foruser-generated material, such as evaluations and tests completed byusers, and documents and assignments prepared by users. In the contextof media distribution and interactive gaming, the user data server 114may store and process resource access data for multiple users (e.g.,content titles accessed, access times, data usage amounts, gaminghistories, user devices and device types, etc.).

Administrator server 116 may include hardware and software components toinitiate various administrative functions at the content managementserver 102 and other components within the content distribution network100. For example, the administrator server 116 may monitor device statusand performance for the various servers, data stores, and/or userdevices 106 in the content distribution network 100. When necessary, theadministrator server 116 may add or remove devices from the network 100,and perform device maintenance such as providing software updates to thedevices in the network 100. Various administrative tools on theadministrator server 116 may allow authorized users to set user accesspermissions to various content resources, monitor resource usage byusers and devices 106, and perform analyses and generate reports onspecific network users and/or devices (e.g., resource usage trackingreports, training evaluations, etc.).

The content distribution network 100 may include one or morecommunication networks 120. Although only a single network 120 isidentified in FIG. 1, the content distribution network 100 may includeany number of different communication networks between any of thecomputer servers and devices shown in FIG. 1 and/or other devicesdescribed herein. Communication networks 120 may enable communicationbetween the various computing devices, servers, and other components ofthe content distribution network 100. As discussed below, variousimplementations of content distribution networks 100 may employdifferent types of networks 120, for example, computer networks,telecommunications networks, wireless networks, and/or any combinationof these and/or other networks.

With reference to FIG. 2, an illustrative distributed computingenvironment 200 is shown including a computer server 202, four clientcomputing devices 206, and other components that may implement certainembodiments and features described herein. In some embodiments, theserver 202 may correspond to the content management server 102 discussedabove in FIG. 1, and the client computing devices 206 may correspond tothe user devices 106. However, the computing environment 200 illustratedin FIG. 2 may correspond to any other combination of devices and serversconfigured to implement a client-server model or other distributedcomputing architecture.

Client devices 206 may be configured to receive and execute clientapplications over one or more networks 220. Such client applications maybe web browser based applications and/or standalone softwareapplications, such as mobile device applications. Server 202 may becommunicatively coupled with the client devices 206 via one or morecommunication networks 220. Client devices 206 may receive clientapplications from server 202 or from other application providers (e.g.,public or private application stores). Server 202 may be configured torun one or more server software applications or services, for example,web-based or cloud-based services, to support content distribution andinteraction with client devices 206. Users operating client devices 206may in turn utilize one or more client applications (e.g., virtualclient applications) to interact with server 202 to utilize the servicesprovided by these components.

Various different subsystems and/or components 204 may be implemented onserver 202. Users operating the client devices 206 may initiate one ormore client applications to use services provided by these subsystemsand components. The subsystems and components within the server 202 andclient devices 206 may be implemented in hardware, firmware, software,or combinations thereof. Various different system configurations arepossible in different distributed computing systems 200 and contentdistribution networks 100. The embodiment shown in FIG. 2 is thus oneexample of a distributed computing system and is not intended to belimiting.

Although exemplary computing environment 200 is shown with four clientcomputing devices 206, any number of client computing devices may besupported. Other devices, such as specialized sensor devices, etc., mayinteract with client devices 206 and/or server 202.

As shown in FIG. 2, various security and integration components 208 maybe used to send and manage communications between the server 202 anduser devices 206 over one or more communication networks 220. Thesecurity and integration components 208 may include separate servers,such as web servers and/or authentication servers, and/or specializednetworking components, such as firewalls, routers, gateways, loadbalancers, and the like. In some cases, the security and integrationcomponents 208 may correspond to a set of dedicated hardware and/orsoftware operating at the same physical location and under the controlof same entities as server 202. For example, components 208 may includeone or more dedicated web servers and network hardware in a datacenteror a cloud infrastructure. In other examples, the security andintegration components 208 may correspond to separate hardware andsoftware components which may be operated at a separate physicallocation and/or by a separate entity.

Security and integration components 208 may implement various securityfeatures for data transmission and storage, such as authenticating usersand restricting access to unknown or unauthorized users. In variousimplementations, security and integration components 208 may provide,for example, a file-based integration scheme or a service-basedintegration scheme for transmitting data between the various devices inthe content distribution network 100. Security and integrationcomponents 208 also may use secure data transmission protocols and/orencryption for data transfers, for example, File Transfer Protocol(FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy(PGP) encryption.

In some embodiments, one or more web services may be implemented withinthe security and integration components 208 and/or elsewhere within thecontent distribution network 100. Such web services, includingcross-domain and/or cross-platform web services, may be developed forenterprise use in accordance with various web service standards, such asRESTful web services (i.e., services based on the Representation StateTransfer (REST) architectural style and constraints), and/or webservices designed in accordance with the Web Service Interoperability(WS-I) guidelines. Some web services may use the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between the server 202 and user devices 206. SSL or TLS mayuse HTTP or HTTPS to provide authentication and confidentiality. Inother examples, web services may be implemented using REST over HTTPSwith the OAuth open standard for authentication, or using theWS-Security standard which provides for secure SOAP messages using XMLencryption. In other examples, the security and integration components208 may include specialized hardware for providing secure web services.For example, security and integration components 208 may include securenetwork appliances having built-in features such as hardware-acceleratedSSL and HTTPS, WS-Security, and firewalls. Such specialized hardware maybe installed and configured in front of any web servers, so that anyexternal devices may communicate directly with the specialized hardware.

Communication network(s) 220 may be any type of network familiar tothose skilled in the art that can support data communications using anyof a variety of commercially-available protocols, including withoutlimitation, TCP/IP (transmission control protocol/Internet protocol),SNA (systems network architecture), IPX (Internet packet exchange),Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols,Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text TransferProtocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and thelike. Merely by way of example, network(s) 220 may be local areanetworks (LAN), such as one based on Ethernet, Token-Ring and/or thelike. Network(s) 220 also may be wide-area networks, such as theInternet. Networks 220 may include telecommunication networks such as apublic switched telephone networks (PSTNs), or virtual networks such asan intranet or an extranet. Infrared and wireless networks (e.g., usingthe Institute of Electrical and Electronics (IEEE) 802.11 protocol suiteor other wireless protocols) also may be included in networks 220.

Computing environment 200 also may include one or more data stores 210and/or back-end servers 212. In certain examples, the data stores 210may correspond to data store server(s) 104 discussed above in FIG. 1,and back-end servers 212 may correspond to the various back-end servers112-116. Data stores 210 and servers 212 may reside in the samedatacenter or may operate at a remote location from server 202. In somecases, one or more data stores 210 may reside on a non-transitorystorage medium within the server 202. Other data stores 210 and back-endservers 212 may be remote from server 202 and configured to communicatewith server 202 via one or more networks 220. In certain embodiments,data stores 210 and back-end servers 212 may reside in a storage-areanetwork (SAN), or may use storage-as-a-service (STaaS) architecturalmodel.

With reference to FIG. 3, an illustrative set of data stores and/or datastore servers are shown, corresponding to the data store servers 104 ofthe content distribution network 100 discussed above in FIG. 1. One ormore individual data stores 301-311 may reside in storage on a singlecomputer server 104 (or a single server farm or cluster) under thecontrol of a single entity, or may reside on separate servers operatedby different entities and/or at remote locations. In some embodiments,data stores 301-311 may be accessed by the content management server 102and/or other devices and servers within the network 100 (e.g., userdevices 106, supervisor devices 110, administrator servers 116, etc.).Access to one or more of the data stores 301-311 may be limited ordenied based on the processes, user credentials, and/or devicesattempting to interact with the data store.

The paragraphs below describe examples of specific data stores that maybe implemented within some embodiments of a content distribution network100. It should be understood that the below descriptions of data stores301-311, including their functionality and types of data stored therein,are illustrative and non-limiting. Data stores server architecture,design, and the execution of specific data stores 301-311 may depend onthe context, size, and functional requirements of a content distributionnetwork 100. For example, in content distribution systems 100 used forprofessional training and educational purposes, separate databases orfile-based storage systems may be implemented in data store server(s)104 to store trainee and/or student data, trainer and/or professor data,training module data and content descriptions, training results,evaluation data, and the like. In contrast, in content distributionsystems 100 used for media distribution from content providers tosubscribers, separate data stores may be implemented in data storesserver(s) 104 to store listings of available content titles anddescriptions, content title usage statistics, subscriber profiles,account data, payment data, network usage statistics, etc.

A user profile data store 301, also referred to herein as a user profiledatabase 301, may include information relating to the end users withinthe content distribution network 100. This information may include usercharacteristics such as the user names, access credentials (e.g., loginsand passwords), user preferences, and information relating to anyprevious user interactions within the content distribution network 100(e.g., requested content, posted content, content modules completed,training scores or evaluations, other associated users, etc.). In someembodiments, this information can relate to one or several individualend users such as, for example, one or several students, teachers,administrators, or the like, and in some embodiments, this informationcan relate to one or several institutional end users such as, forexample, one or several schools, groups of schools such as one orseveral school districts, one or several colleges, one or severaluniversities, one or several training providers, or the like.

In some embodiments in which the one or several end users areindividuals, and specifically are students, the user profile database301 can further include information relating to these students' academicand/or educational history, which academic and/or educational historycan be temporally divided and/or temporally dividable. In someembodiments, this academic and/or education history can be temporallydivided and/or temporally dividable into recent and non-recent data. Insome embodiments, data can be recent when it has been captured and/orgenerated within the past two years, within the past year, within thepast six months, within the past three months, within the past month,within the past two weeks, within the past week, within the past threedays, within the past day, within the past 12 hours, within the past sixhours, within the past three hours, and/or within any other orintermediate time period.

In some embodiments, the information within the user profile database301 can identify one or several courses of study that the student hasinitiated, completed, and/or partially completed, as well as gradesreceived in those courses of study. In some embodiments, the student'sacademic and/or educational history can further include informationidentifying student performance on one or several tests, quizzes, and/orassignments. In some embodiments, this information can be stored in atier of memory that is not the fastest memory in the content deliverynetwork 100.

The user profile database 301 can include information relating to one orseveral student learning preferences. In some embodiments, for example,the student may have one or several preferred learning styles, one orseveral most effective learning styles, and/or the like. In someembodiments, the students learning style can be any learning styledescribing how the student best learns or how the student prefers tolearn. In one embodiment, these learning styles can include, forexample, identification of the student as an auditory learner, as avisual learner, and/or as a tactile learner. In some embodiments, thedata identifying one or several student learning styles can include dataidentifying a learning style based on the student's educational historysuch as, for example, identifying a student as an auditory learner whenthe student has received significantly higher grades and/or scores onassignments and/or in courses favorable to auditory learners. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

In some embodiments, the data in the use profile database 301 can besegregated and/or divided based on one or several attributes of the datasuch as, for example, the age of the data, the content relating to whichthe data was collected, or the like. In some embodiments, this data canidentify one or several correct or incorrect answers provided by thestudent, the expected number of correct or incorrect answers provided bythe student, student response times, student learning styles, or thelike. In some embodiments, the student database can include a dataacceptance curve that can define an expected learning trajectory for oneor several students based on historic data for those one or severalstudents and, in some embodiments, also based on one or severalpredictive models. A data acceptance curve for one or several users isreferred to herein as a user data acceptance curve. In some embodiments,the user data acceptance curve can comprise a plurality of dataacceptance curves calculated for data packets of different difficultiesand/or a plurality of data acceptance curves calculated for data packetshaving different subject matters or skills.

The user profile database 301 can further include information relatingto one or several teachers and/or instructors who are responsible fororganizing, presenting, and/or managing the presentation of informationto the student. In some embodiments, user profile database 301 caninclude information identifying courses and/or subjects that have beentaught by the teacher, data identifying courses and/or subjectscurrently taught by the teacher, and/or data identifying courses and/orsubjects that will be taught by the teacher. In some embodiments, thiscan include information relating to one or several teaching styles ofone or several teachers. In some embodiments, the user profile database301 can further include information indicating past evaluations and/orevaluation reports received by the teacher. In some embodiments, theuser profile database 301 can further include information relating toimprovement suggestions received by the teacher, training received bythe teacher, continuing education received by the teacher, and/or thelike. In some embodiments, this information can be stored in a tier ofmemory that is not the fastest memory in the content delivery network100.

An accounts data store 302, also referred to herein as an accountsdatabase 302, may generate and store account data for different users invarious roles within the content distribution network 100. For example,accounts may be created in an accounts data store 302 for individual endusers, supervisors, administrator users, and entities such as companiesor educational institutions. Account data may include account types,current account status, account characteristics, and any parameters,limits, restrictions associated with the accounts.

A content library data store 303, also referred to herein as a contentlibrary database 303, may include information describing the individualdata packets, also referred to herein as content items or contentresources available via the content distribution network 100. As usedherein, a data packet is a group of data providable to a user such as,data for teaching a skill or conveying knowledge and/or for assessing askill or knowledge. In some embodiments, the library data store 303 mayinclude metadata, properties, and other characteristics associated withthe content resources stored in the content server 112. Such data mayidentify one or more aspects or content attributes of the associatedcontent resources, for example, subject matter, access level, or skilllevel of the content resources, license attributes of the contentresources (e.g., any limitations and/or restrictions on the licensableuse and/or distribution of the content resource), price attributes ofthe content resources (e.g., a price and/or price structure fordetermining a payment amount for use or distribution of the contentresource), rating attributes for the content resources (e.g., dataindicating the evaluation or effectiveness of the content resource), andthe like. In some embodiments, the library data store 303 may beconfigured to allow updating of content metadata or properties, and toallow the addition and/or removal of information relating to the contentresources. For example, content relationships may be implemented asgraph structures, which may be stored in the library data store 303 orin an additional store for use by selection algorithms along with theother metadata.

In some embodiments, the content library database 303 can include aplurality of databases such as, for example, a structure database, anaggregate database, a data packet database, also referred to herein as acontent item database, which can include a content database and aquestion database, and a content response database. The content responsedatabase can include information used to determine whether a userresponse to a question was correct or incorrect. In some embodiments,the content response database can further include raw question dataincluding information relating to correct or incorrect responsesprovided by users and user data associated with some or all of thoseresponses. In some embodiments, this user data can identify one orseveral attributes of the user such as, for example, any of the userproperties discussed above in the user profile database 301. In someembodiments, the content response database can include raw question dataincluding information relating to correct or incorrect responsesprovided by users and one or several pointers pointing to those users'data in the user profile database 301.

The content item database can include one or several data packets. Insome embodiments, the one or several data packets can include, forexample, data packets for conveying information to a user, also referredto herein as delivery data packets or delivery content items, and datapackets to assess a knowledge level and/or skill level of the user, alsoreferred to as assessment data packets or assessment content items.These data packets can include one or several character strings, text,images, audio, video, or the like. In some embodiments, data packets toconvey information to the user can include one or severaldemonstrations, lectures, readings, lessons, videos or video clips,recordings, audio clips, or the like, and in some embodiments, the datapackets to assess a knowledge level and/or skill level of the user caninclude one or several questions including, for example, one or severalshort answer questions, essay questions, multiple choice questions,true/false questions, or the like. In some embodiments, the data packetsto convey information can be stored in the content database of the datapacket database, and in some embodiments, the data packets to assess theknowledge level and/or skill level of the user can be stored in thequestion database of the data packet database.

In some embodiments, the content item database can include a dataacceptance curve that can define an expected learning trajectory for oneor several data packets based on historic data for those one or severaldata packets and, in some embodiments, also based on one or severalpredictive models. A data acceptance curve for one or several datapackets is referred to herein as a data packet acceptance curve. In someembodiments, the data packet acceptance curve can comprise a pluralityof data acceptance curves calculated for with data packet user datacollected from one or several users. In some embodiments, these one orseveral user can have different attributes such as different skilllevels, different learning styles, or the like.

The aggregate database can include a grouping of data packets including,for example, and grouping of one or both of content items to conveyinformation to a user and content items to assess a knowledge leveland/or skill level of the user. This grouping of data packets can becreated by a user such as a teacher, instructor, supervisor, or thelike. In some embodiments, this grouping of data packets can be createdby the user via the supervisor device. In some embodiments, thisgrouping of data packets can be a lesson that can be given to one orseveral users.

The structure database can include data identifying a content structureor a knowledge structure that interrelates and interlinks content thatcan be, for example, stored in others of the databases of, for example,the content library database 303. In some embodiments, for example, thecontent structure can identify one or several groupings of data packetsor grouping categories by which one or several data packets can beidentified and/or related. These groupings of data packets can be formedbased on the existence or degree of existence of one or several sharedattributes among the grouped data packets. In some embodiments, theseone or several shared attributes can include, for example, the contentof the grouped data packets including one or both of: (1) information iscontained in the grouped data packets; and (2) how information isconveyed by the grouped data packets (e.g. text, video, image(s), audio,etc.), a skill or skill level of the grouped data packets, of the like.

In some embodiments, this content can be stored directly in thestructure database, and in some embodiments, the structure database cancomprise one or several pointers pointing to other databases containingthe appropriate content. Thus, in some embodiments, the structuredatabase can contain one or several lessons, one or several questions,and/or one or several answers, some or all of which can be organizedand/or connected. In some embodiments, the knowledge database cancomprise one or several pointers pointing to one or several lessons inthe aggregate database, one or several pointers pointing to one orseveral data packets that can be, for example, questions, in the datapacket database, and/or one or several pointers pointing to one orseveral responses in the content response database. In some embodiments,and in response to a request from the content management server 102, thepointers in the structure database can be referenced and the desireddata can be retrieved from its locations.

In some embodiments, the structure database can further include one orseveral external content structures. In some embodiments, these externalcontent structures can be pre-existing and can be linked to one orseveral data packets. In some embodiments, the external contentstructure can be used to provide an initial organization of the datapackets, which initial organization can be altered and/or refined basedon information collected from one or several users in response toreceipt of one or several of the data packets.

A pricing data store 304, also referred to herein as a pricing database,may include pricing information and/or pricing structures fordetermining payment amounts for providing access to the contentdistribution network 100 and/or the individual content resources withinthe network 100. In some cases, pricing may be determined based on auser's access to the content distribution network 100, for example, atime-based subscription fee, or pricing based on network usage. In othercases, pricing may be tied to specific content resources. Certaincontent resources may have associated pricing information, whereas otherpricing determinations may be based on the resources accessed, theprofiles and/or accounts of the user and the desired level of access(e.g., duration of access, network speed, etc.). Additionally, thepricing data store 304 may include information relating to compilationpricing for groups of content resources, such as group prices and/orprice structures for groupings of resources.

A license data store 305, also referred to herein as a license database,may include information relating to licenses and/or licensing of thecontent resources within the content distribution network 100. Forexample, the license data store 305 may identify licenses and licensingterms for individual content resources and/or compilations of contentresources in the content server 112, the rights holders for the contentresources, and/or common or large-scale right holder information such ascontact information for rights holders of content not included in thecontent server 112.

A content access data store 306, also referred to herein as a contentaccess database, may include access rights and security information forthe content distribution network 100 and specific content resources. Forexample, the content access data store 306 may include login information(e.g., user identifiers, logins, passwords, etc.) that can be verifiedduring user login attempts to the network 100. The content access datastore 306 also may be used to store assigned user roles and/or userlevels of access. For example, a user's access level may correspond tothe sets of content resources and/or the client or server applicationsthat the user is permitted to access. Certain users may be permitted ordenied access to certain applications and resources based on theirsubscription level, training program, course/grade level, etc. Certainusers may have supervisory access over one or more end users, allowingthe supervisor to access all or portions of the end user's content,activities, evaluations, etc. Additionally, certain users may haveadministrative access over some users and/or some applications in thecontent management network 100, allowing such users to add and removeuser accounts, modify user access permissions, perform maintenanceupdates on software and servers, etc.

A source data store 307, also referred to herein as a source database,may include information relating to the source of the content resourcesavailable via the content distribution network. For example, a sourcedata store 307 may identify the authors and originating devices ofcontent resources, previous pieces of data and/or groups of dataoriginating from the same authors or originating devices, and the like.

An evaluation data store 308, also referred to herein as an evaluationdatabase, may include information used to direct the evaluation of usersand content resources in the content management network 100. In someembodiments, the evaluation data store 308 may contain, for example, theanalysis criteria and the analysis guidelines for evaluating users(e.g., trainees/students, gaming users, media content consumers, etc.)and/or for evaluating the content resources in the network 100. Theevaluation data store 308 also may include information relating toevaluation processing tasks, for example, the identification of usersand user devices 106 that have received certain content resources oraccessed certain applications, the status of evaluations or evaluationhistories for content resources, users, or applications, and the like.Evaluation criteria may be stored in the evaluation data store 308including data and/or instructions in the form of one or severalelectronic rubrics or scoring guides for use in the evaluation of thecontent, users, or applications. The evaluation data store 308 also mayinclude past evaluations and/or evaluation analyses for users, content,and applications, including relative rankings, characterizations,explanations, and the like.

A model data store 309, also referred to herein as a model database, canstore information relating to one or several predictive models. Thepredictive models can include, for example, a Rasch model, an itemresponse model, a Performance Factor Analysis model, Knowledge Tracing,one or several statistical models, including, for example, one orseveral normal models, or the like. In some embodiments, the predictivemodels can be generally applicable to any user of the contentdistribution network 100 or components thereof, or to content stored inthe content distribution network 100. In some embodiments, thepredictive models can be customized for one or several selected userand/or selected content.

A threshold database 310, also referred to herein as a thresholddatabase, can store one or several threshold values. These one orseveral threshold values can delineate between states or conditions. Inone exemplary embodiment, for example, a threshold value can delineatebetween an acceptable user performance and an unacceptable userperformance, between content appropriate for a user and content that isinappropriate for a user, or the like.

In addition to the illustrative data stores described above, data storeserver(s) 104 (e.g., database servers, file-based storage servers, etc.)may include one or more external data aggregators 311. External dataaggregators 311 may include third-party data sources accessible to thecontent management network 100, but not maintained by the contentmanagement network 100. External data aggregators 311 may include anyelectronic information source relating to the users, content resources,or applications of the content distribution network 100. For example,external data aggregators 311 may be third-party data stores containingdemographic data, education related data, consumer sales data, healthrelated data, and the like. Illustrative external data aggregators 311may include, for example, social networking web servers, public recordsdata stores, learning management systems, educational institutionservers, business servers, consumer sales data stores, medical recorddata stores, etc. Data retrieved from various external data aggregators311 may be used to verify and update user account information, suggestuser content, and perform user and content evaluations.

With reference now to FIG. 4, a block diagram is shown illustrating anembodiment of one or more content management servers 102 within acontent distribution network 100. As discussed above, content managementserver(s) 102 may include various server hardware and softwarecomponents that manage the content resources within the contentdistribution network 100 and provide interactive and adaptive content tousers on various user devices 106. For example, content managementserver(s) 102 may provide instructions to and receive information fromthe other devices within the content distribution network 100, in orderto manage and transmit content resources, user data, and server orclient applications executing within the network 100.

A content management server 102 may include a content customizationsystem 402. The content customization system 402 may be implementedusing dedicated hardware within the content distribution network 100(e.g., a content customization server 402), or using designated hardwareand software resources within a shared content management server 102. Insome embodiments, the content customization system 402 may adjust theselection and adaptive capabilities of content resources to match theneeds and desires of the users receiving the content. For example, thecontent customization system 402 may query various data stores andservers 104 to retrieve user information, such as user preferences andcharacteristics (e.g., from a user profile data store 301), user accessrestrictions to content recourses (e.g., from a content access datastore 306), previous user results and content evaluations (e.g., from anevaluation data store 308), and the like. Based on the retrievedinformation from data stores 104 and other data sources, the contentcustomization system 402 may modify content resources for individualusers.

A content management server 102 also may include a user managementsystem 404. The user management system 404 may be implemented usingdedicated hardware within the content distribution network 100 (e.g., auser management server 404), or using designated hardware and softwareresources within a shared content management server 102. In someembodiments, the user management system 404 may monitor the progress ofusers through various types of content resources and groups, such asmedia compilations, courses or curriculums in training or educationalcontexts, interactive gaming environments, and the like. For example,the user management system 404 may query one or more databases and/ordata store servers 104 to retrieve user data such as associated contentcompilations or programs, content completion status, user goals,results, and the like.

A content management server 102 also may include an evaluation system406. The evaluation system 406 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., anevaluation server 406), or using designated hardware and softwareresources within a shared content management server 102. The evaluationsystem 406 may be configured to receive and analyze information fromuser devices 106. For example, various ratings of content resourcessubmitted by users may be compiled and analyzed, and then stored in adata store (e.g., a content library data store 303 and/or evaluationdata store 308) associated with the content. In some embodiments, theevaluation server 406 may analyze the information to determine theeffectiveness or appropriateness of content resources with, for example,a subject matter, an age group, a skill level, or the like. In someembodiments, the evaluation system 406 may provide updates to thecontent customization system 402 or the user management system 404, withthe attributes of one or more content resources or groups of resourceswithin the network 100. The evaluation system 406 also may receive andanalyze user evaluation data from user devices 106, supervisor devices110, and administrator servers 116, etc. For instance, evaluation system406 may receive, aggregate, and analyze user evaluation data fordifferent types of users (e.g., end users, supervisors, administrators,etc.) in different contexts (e.g., media consumer ratings, trainee orstudent comprehension levels, teacher effectiveness levels, gamer skilllevels, etc.).

A content management server 102 also may include a content deliverysystem 408. The content delivery system 408 may be implemented usingdedicated hardware within the content distribution network 100 (e.g., acontent delivery server 408), or using designated hardware and softwareresources within a shared content management server 102. The contentdelivery system 408 may receive content resources from the contentcustomization system 402 and/or from the user management system 404, andprovide the resources to user devices 106. The content delivery system408 may determine the appropriate presentation format for the contentresources based on the user characteristics and preferences, and/or thedevice capabilities of user devices 106. If needed, the content deliverysystem 408 may convert the content resources to the appropriatepresentation format and/or compress the content before transmission. Insome embodiments, the content delivery system 408 may also determine theappropriate transmission media and communication protocols fortransmission of the content resources.

In some embodiments, the content delivery system 408 may includespecialized security and integration hardware 410, along withcorresponding software components to implement the appropriate securityfeatures content transmission and storage, to provide the supportednetwork and client access models, and to support the performance andscalability requirements of the network 100. The security andintegration layer 410 may include some or all of the security andintegration components 208 discussed above in FIG. 2, and may controlthe transmission of content resources and other data, as well as thereceipt of requests and content interactions, to and from the userdevices 106, supervisor devices 110, administrative servers 116, andother devices in the network 100.

With reference now to FIG. 5, a block diagram of an illustrativecomputer system is shown. The system 500 may correspond to any of thecomputing devices or servers of the content distribution network 100described above, or any other computing devices described herein. Inthis example, computer system 500 includes processing units 504 thatcommunicate with a number of peripheral subsystems via a bus subsystem502. These peripheral subsystems include, for example, a storagesubsystem 510, an I/O subsystem 526, and a communications subsystem 532.

Bus subsystem 502 provides a mechanism for letting the variouscomponents and subsystems of computer system 500 communicate with eachother as intended. Although bus subsystem 502 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 502 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Sucharchitectures may include, for example, an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 504, which may be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 500. One or more processors,including single core and/or multicore processors, may be included inprocessing unit 504. As shown in the figure, processing unit 504 may beimplemented as one or more independent processing units 506 and/or 508with single or multicore processors and processor caches included ineach processing unit. In other embodiments, processing unit 504 may alsobe implemented as a quad-core processing unit or larger multicoredesigns (e.g., hexa-core processors, octo-core processors, ten-coreprocessors, or greater.

Processing unit 504 may execute a variety of software processes embodiedin program code, and may maintain multiple concurrently executingprograms or processes. At any given time, some or all of the programcode to be executed can be resident in processor(s) 504 and/or instorage subsystem 510. In some embodiments, computer system 500 mayinclude one or more specialized processors, such as digital signalprocessors (DSPs), outboard processors, graphics processors,application-specific processors, and/or the like.

I/O subsystem 526 may include device controllers 528 for one or moreuser interface input devices and/or user interface output devices 530.User interface input and output devices 530 may be integral with thecomputer system 500 (e.g., integrated audio/video systems, and/ortouchscreen displays), or may be separate peripheral devices which areattachable/detachable from the computer system 500.

Input devices 530 may include a keyboard, pointing devices such as amouse or trackball, a touchpad or touch screen incorporated into adisplay, a scroll wheel, a click wheel, a dial, a button, a switch, akeypad, audio input devices with voice command recognition systems,microphones, and other types of input devices. Input devices 530 mayalso include three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices. Additionalinput devices 530 may include, for example, motion sensing and/orgesture recognition devices that enable users to control and interactwith an input device through a natural user interface using gestures andspoken commands, eye gesture recognition devices that detect eyeactivity from users and transform the eye gestures as input into aninput device, voice recognition sensing devices that enable users tointeract with voice recognition systems through voice commands, medicalimaging input devices, MIDI keyboards, digital musical instruments, andthe like.

Output devices 530 may include one or more display subsystems, indicatorlights, or non-visual displays such as audio output devices, etc.Display subsystems may include, for example, cathode ray tube (CRT)displays, flat-panel devices, such as those using a liquid crystaldisplay (LCD) or plasma display, light-emitting diode (LED) displays,projection devices, touch screens, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system500 to a user or other computer. For example, output devices 530 mayinclude, without limitation, a variety of display devices that visuallyconvey text, graphics and audio/video information such as monitors,printers, speakers, headphones, automotive navigation systems, plotters,voice output devices, and modems.

Computer system 500 may comprise one or more storage subsystems 510,comprising hardware and software components used for storing data andprogram instructions, such as system memory 518 and computer-readablestorage media 516. The system memory 518 and/or computer-readablestorage media 516 may store program instructions that are loadable andexecutable on processing units 504, as well as data generated during theexecution of these programs.

Depending on the configuration and type of computer system 500, systemmemory 518 may be stored in volatile memory (such as random accessmemory (RAM) 512) and/or in non-volatile storage drives 514 (such asread-only memory (ROM), flash memory, etc.). The RAM 512 may containdata and/or program modules that are immediately accessible to and/orpresently being operated and executed by processing units 504. In someimplementations, system memory 518 may include multiple different typesof memory, such as static random access memory (SRAM) or dynamic randomaccess memory (DRAM). In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 500, such as duringstart-up, may typically be stored in the non-volatile storage drives514. By way of example, and not limitation, system memory 518 mayinclude application programs 520, such as client applications, Webbrowsers, mid-tier applications, server applications, etc., program data522, and an operating system 524.

Storage subsystem 510 also may provide one or more tangiblecomputer-readable storage media 516 for storing the basic programmingand data constructs that provide the functionality of some embodiments.Software (programs, code modules, instructions) that when executed by aprocessor provide the functionality described herein may be stored instorage subsystem 510. These software modules or instructions may beexecuted by processing units 504. Storage subsystem 510 may also providea repository for storing data used in accordance with the presentinvention.

Storage subsystem 510 may also include a computer-readable storage mediareader that can further be connected to computer-readable storage media516. Together and, optionally, in combination with system memory 518,computer-readable storage media 516 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 516 containing program code, or portionsof program code, may include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computer system 500.

By way of example, computer-readable storage media 516 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 516 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 516 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 500.

Communications subsystem 532 may provide a communication interface fromcomputer system 500 and external computing devices via one or morecommunication networks, including local area networks (LANs), wide areanetworks (WANs) (e.g., the Internet), and various wirelesstelecommunications networks. As illustrated in FIG. 5, thecommunications subsystem 532 may include, for example, one or morenetwork interface controllers (NICs) 534, such as Ethernet cards,Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as wellas one or more wireless communications interfaces 536, such as wirelessnetwork interface controllers (WNICs), wireless network adapters, andthe like. Additionally and/or alternatively, the communicationssubsystem 532 may include one or more modems (telephone, satellite,cable, ISDN), synchronous or asynchronous digital subscriber line (DSL)units, FireWire® interfaces, USB® interfaces, and the like.Communications subsystem 536 also may include radio frequency (RF)transceiver components for accessing wireless voice and/or data networks(e.g., using cellular telephone technology, advanced data networktechnology, such as 3G, 4G or EDGE (enhanced data rates for globalevolution), WiFi (IEEE 802.11 family standards, or other mobilecommunication technologies, or any combination thereof), globalpositioning system (GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 532 maybe detachable components coupled to the computer system 500 via acomputer network, a FireWire® bus, or the like, and/or may be physicallyintegrated onto a motherboard of the computer system 500. Communicationssubsystem 532 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 532 may also receive inputcommunication in the form of structured and/or unstructured data feeds,event streams, event updates, and the like, on behalf of one or moreusers who may use or access computer system 500. For example,communications subsystem 532 may be configured to receive data feeds inreal-time from users of social networks and/or other communicationservices, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources(e.g., data aggregators 309). Additionally, communications subsystem 532may be configured to receive data in the form of continuous datastreams, which may include event streams of real-time events and/orevent updates (e.g., sensor data applications, financial tickers,network performance measuring tools, clickstream analysis tools,automobile traffic monitoring, etc.). Communications subsystem 532 mayoutput such structured and/or unstructured data feeds, event streams,event updates, and the like to one or more data stores 104 that may bein communication with one or more streaming data source computerscoupled to computer system 500.

Due to the ever-changing nature of computers and networks, thedescription of computer system 500 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software, or acombination. Further, connection to other computing devices, such asnetwork input/output devices, may be employed. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

With reference now to FIG. 6, exemplary images of data acceptance curves600 which can be either user data acceptance curves or data packetacceptance curves. Each of these images includes an axis labeled a“p(correct)” which identifies a percent of time that a desired responseis received for a next provided data packet. Each of these images alsoincludes an axis labeled “number of corrects” which identifies thenumber of desired responses provided to a category or group of datapackets. Each of the images further include indicators 602 that identifyaggregated data for one or several users and a trend-line 604 depictingexpected user progress based on the some or all of the data representedby the indicators 602.

In some embodiments, a data acceptance curve 600 can be generated forone or several users. In such an embodiment, the data acceptance curve600 can be based on the interactions of these one or several users withthe content distribution network 100, and particularly based on theresponses provided by these one or several users to assessment datapackets provided by the content distribution network 100. This user dataacceptance curve can identify how these one or several users accept datawhich can include, for example, develop a new skill or skill set, masteror learn subject matter, or the like. In such an embodiment, one orseveral users who have a higher data acceptance rate are indicated by arelatively steeper slope of the trend-line 604 than one or several userswith a lower data acceptance rate.

In some embodiments, a data acceptance curve 600 can be generated forone or several data packets. Such content item data acceptance curvescan be stored in the content library database 303. In such anembodiment, the data acceptance curve 600 can identify the acceptance ofthe data of the one or several data packets, particularly vis-à-vis thecurrent grouping of the one or several data packets. This acceptancecurve can thus depict the relationship between exposure to related datapackets and the likelihood of providing a desired response to the one orseveral data packets for which the data acceptance curve 600 is created.

The data acceptance curve 600 can be generated with data for one orseveral users and can be, for example, associated in one of thedatabases with those one or several users or with one or several datapackets. In such an embodiment, the data acceptance curve can provideinformation relating to an expected outcome for one or several users asthe one or several users provide additional desired responses. In suchembodiments, the slope of the trend-line 604 can provide an indicator ofthis expected progress, and specifically, a steep, positive trend-line604 indicates that a user has historically progressed and/or processeddata packets more rapidly than a relatively less steep, positivetrend-line 604.

In some embodiments, the data acceptance curves 600 can further providean indicator between the correlation between data packets, andspecifically between one or both of a plurality of assessment datapackets and one or several delivery data packets. In some embodiments,for example, a relatively less positively sloped trend-line can indicatea weaker correlation between one or several topics, subjects, and/orskills of a plurality of data packets.

Accordingly, and referring to images (a), (b), and (c) of FIG. 6, image(a) indicates a relatively stronger correlation between the provideddata packets associated with the response data represented in image (a)than the correlation of either images (b) and (c), and image (a)likewise indicates that the one or several users associated with theresponse data represented in image (a) process data faster than thoseusers of images (b) and (c). Similarly, image (c) indicates a relativelyweaker correlation between the provided data packets associated with theresponse data represented in image (c) than the correlation of eitherimages (a) and (b), and image (c) likewise indicates that the one orseveral users associated with the response data represented in image (c)process data slower than those users of images (a) and (b).

With reference now to FIG. 7, a plurality of images of data packetcurves 700, also referred to herein as content item curves 700, areshown. The data packets curves can be stored in the content librarydatabase 303 and can be, for example, associated with the data packet(s)for which they were created. Each of the images of content item curvesin FIG. 7 includes an axis labeled a “p(correct)” which identifies aprobability of receiving a desired response to the data packet for whichthe data packet curve 700 is created. Each of these images furtherincludes an axis labeled “ability θ” that identifies a skill level ofone or several users who have responded to the data packet associatedwith the content item curve 700.

In some embodiments, the data packet curve 700 can include informationidentifying one or several properties of the data packet including, forexample, the difficulty of the data packet, the differentiation of thedata packet, and a randomness measure or randomness parameter of thedata packet.

In some embodiments, the difficulty of the data packet correlates to theprobability of a user providing a desired response to the data packet.Thus, a user, regardless of skill level, will have a lower likelihood ofproviding the desired response to a first data packet that has a higherdifficulty than the likelihood of that user providing the desiredresponse to a second, relatively easier data packet. In someembodiments, the placement of the data packet along the axis labeled“ability θ” indicates the difficulty of the data packet, the width ofthe curve such indicates the differentiation of the data packet, and theabsolute minimum of the data packet curve 700 indicates the randomnessmeasure of the data packet. Thus, the data packet associated with image(a) is more difficult than the data packet associated with image (b).

In some embodiments, the differentiation of a content item describes thedegree to which correct and incorrect responses to a data packetdistinguish between skill levels. Thus, a data packet having a greaterlevel of differentiation will, across a data set, better distinguishbetween user skill levels than a data packet having a lower level ofdifferentiation. In some embodiments, the differentiation of a datapacket is indicated by the range of ability levels corresponding tonon-asymptotic portion of the data packet curve 700, or more generally,by the width of the non-asymptotic portion of the data packet curve 700.Accordingly, both the data packets associated with images (a) and (b)have approximately equal levels of differentiation.

The randomness measure of the data packet characterizes the likelihoodof randomly receiving the desired response from a user to that datapacket. This randomness can be indicated by a vertical displacement ofthe data packet curve 700 along the axis labeled “p(correct).”Accordingly, the data packet associated with image (c) is more greatlyaffected by randomness than the data packets associated with (a) and (b)as a user has a greater than 20 percent chance of providing the desiredresponse, regardless of ability, to the data packet associated withimage (c).

With reference now to FIG. 8, a flowchart illustrating one embodiment ofa process 800 for generating a customized aggregation to maximize datatransmission rates through the content distribution network 100 isshown. The process 800 can be performed by components of the contentdistribution network 100. The process 800 begins at block 842 wherein aaggregation information is received. In some embodiments, theaggregation information can identify one or several data packets,including one or several delivery data packets and/or one or severalassessment data packets, for intended delivery to one or several users,which users can be, for example, one or several students, via, forexample, one or several user devices 106.

The aggregation information can be generated by the content deliverynetwork 100 or can be received from a user-supervisor such as a teacher,and instructor, or any individual, group, or entity responsible forproviding content to one or several other users. In some embodiments,the aggregation information can be received from the user-supervisor viaone or several supervisor devices 110 and one or several communicationnetworks 120.

In some embodiments in which the aggregation information is generated bythe content delivery network 100, the content delivery network canreceive one or several parameters from the user-supervisor. Theseparameters can, for example, specify one or several attributes of thedesired aggregation of data packets for providing to one or severalusers. These attributes can specify, for example, a difficulty level, adifferentiation level, a time parameter which can include, for example,the maximum or average duration for user data acceptance, a subjectmatter parameter which can identify one or several topics, skills, orthe like for inclusion the aggregation, or the like. In someembodiments, these attributes can be applied to either or both of theaggregation as a whole or to some or all of the individual data packetsforming the aggregation.

In some embodiments, these attributes can be received from thesupervisor device 110 via one or several communications networks 120. Inone particular embodiment, the supervisor device 110 can include agraphical user interface (GUI) including features enabling user inputsindicative of these attributes. In one embodiment, for example, the GUIcan include a plurality of sliders each of which corresponds to anattribute. By varying the position of a sliding element along the lengthof the slider, the user-supervisor can adjust attribute levels.

After user inputs indicative of these attributes have been received, thecontent delivery network 100 or its components can identify one orseveral data packets that either alone or in combination correspond tothe specified attributes. Information relating to these one or severaldata packets can be collected and stored in one of the databases suchas, for example, the content library database 303, and these one orseveral data packets can be identified as the aggregation.

After the aggregation information has been received, the process 800proceeds to block 844 wherein the one or several data packets identifiedby the aggregation information are retrieved and/or received. In someembodiments, these one or several data packets can be retrieved orreceived from one of the databases such as the content library database303 by for example, the central processor 102 of the content deliverynetwork 100.

After the data packets have been retrieved, the process 800 proceeds toblock 846 where data packet metadata for the retrieved data packets isretrieved. In some embodiments, the data packet metadata can relate toand/or identify one or several aspects of the content of the datapacket. In contrast to the data packet user data, the data packetmetadata does not change based on user interactions with the contentdelivery network 100, and specifically does not change based on userresponses to provided data packets. In some embodiments, contentmetadata can be received for some or all of the data packets received inblock 844. The data packet metadata can be retrieved and/or receivedfrom the content library database 303.

After the data packet metadata is received, the process 800 proceeds toblock 848 where the data packet user data is retrieved or received. Thedata packet user data can include data associated with each of the datapackets, which is based on one or several user interactions with thecontent delivery network 100, and specifically which is based on one orseveral user responses provided to one or several assessment datapackets. In some embodiments, the data packet user data can be retrievedsimultaneous with the retrieval of the data packets, and can beretrieved from the content library database 303.

After the data packet user data has been retrieved, the process 800proceeds to block 850 wherein a recipient cohort is identified. In someembodiments, the recipient cohort is the group of users designated toreceive the aggregation. The recipient cohort can be determined based oninformation received from the user-supervisor and/or based on userinformation stored in, for example, the user profile database 301. Inone embodiment, for example, the recipient cohort can correspond tousers participating in a group such as, for example, the class, aprogram, the training group, or the like. In some embodiments, therecipient cohort can be identified by determining the group forreceiving the aggregation and then determining the users in that group.In one embodiment, for example, the user-supervisor can designate aclass for receiving the aggregation and the users in that class can beidentified based on, for example, enrollment information stored in theuser profile database 301.

After the recipient cohort has been identified, the process 800 proceedsto block 852 wherein recipient cohort user data is received orretrieved. In some embodiments, this recipient cohort user data can bereceived or retrieved from the user profile database 301. Thisinformation can identify one or several attributes of the user and/orcan identify one or several user actions. These attributes can include,for example, the data acceptance rate, a skill level, a learning style,or the like. The one or several user actions can include, for example,information identifying one or several data packets received by theuser, information identifying one or several responses provided by theuser including identifying one or several desired responses or undesiredresponses provided by the user, amount of time spent interacting withthe content distribution network 100, amount of time spent receivingand/or responding to data packets, or the like. In some embodiments,this can include receiving, and/or retrieval of one or several dataacceptance curves associated with one or several of the users in therecipient cohort.

After the recipient cohort user data has been received, the process 800proceeds to block 854 wherein combined aggregation data is generated. Insome embodiments, the combined aggregation data can be generated fromone or both of the data packet metadata retrieved in block 846 and thedata packet user data retrieved in block 848. This combined aggregationdata can characterize an attribute of the aggregation as a whole suchas, for example, the difficulty of the aggregation as a whole, thedifferentiation of the aggregation as a whole, the anticipated timerequired for the aggregation as a whole, or the like. In someembodiments, this combined aggregation data can be generated byidentifying and combining like attributes from the data packets in theaggregation according to known techniques.

After the combined aggregation data has been generated, the process 800proceeds to block 856 wherein combined cohort data is generated. In someembodiments, combined cohort data can be generated from cohort user datareceived an block 852. This combined cohort data can characterize anattribute of the cohort as a whole such as, for example, the dataacceptance rate, the skill level, lapsed time including the time alreadyspent by users and the recipient cohort receiving and/or responding todata packets, or the like. In some embodiments, this combined cohortdata can be generated by identifying and combining the like attributesfrom users and the cohort according to known techniques.

After the combined cohort data has been generated, the process 800proceeds to block 858 wherein the combined aggregation data is comparedto the combined cohort data. In some embodiments, this comparison can beperformed by a component of the content distribution network 100 such asthe central server 102. This comparison can determine whether one orseveral attributes of the aggregation correspond, or more specificallysatisfactorily correspond to one or several attributes of the cohort. Insome embodiments, the content distribution network 100 can determine adifference between one or several attributes indicated in the combinedaggregation data and in the combined cohort data, and can compare thisdifference to a threshold value.

In some embodiments, the comparison of the combined aggregation data andthe combined cohort data can include determining whether the difficultylevel of the aggregation corresponds the skill level of the cohort, ifthe data acceptance rate of the aggregation as adapted to correspond toone or several attributes of the cohort is acceptable, determiningwhether the differentiation level of the aggregation is satisfactory, orthe like.

In some embodiments, for example, the data acceptance rate for theaggregation can be adjusted to correspond to attributes of the users inthe cohort. Specifically, one or several cohort parameters wereparameters relating to users in the cohort can be used to filter dataused in generating the aggregation data acceptance rate such that thedata used in generating the aggregation data acceptance rateapproximates attributes of the cohort as a whole or of individual usersand the cohort. In some embodiments, for example, this can includefiltering user data used in generating the data acceptance rate for theaggregation such that user data for users having similar skill levels tousers in the cohort is included, such that user data acceptance rates ofusers in a cohort corresponds to those in the user data used ingenerating the data acceptance rate, or the like.

After the combined aggregation data and the combined cohort data havebeen compared, the process 800 proceeds to decision block 868 wherein itis determined whether the aggregation is compatible with the cohort. Insome embodiments, this can include determining whether the one orseveral compared attributes of the combined aggregation data in thecombined cohort data satisfactorily match in which case the process 800proceeds to block 862 wherein the aggregation is recommended for use to,for example, the user-supervisor via the supervisor device 110. In someembodiments, this recommendation can further include the providing ofthe aggregation in the data packets included therein to the usersidentified in the recipient cohort of block 850.

Returning again to decision block 868, if it is determined that thecompared one or several attributes of the combined aggregation data inthe combined cohort data do not match either in that they do not attainminimum desired levels or in that they exceed maximum desired levels,the process 800 proceeds to block 864 wherein an alteration isrecommended. In some embodiments, this recommendation can includeidentification of one or several data packets in the aggregation forremoval and/or the identification of one or several data packets forinclusion in the aggregation. In some embodiments, the recommendation ofthe alteration can be provided to the user-supervisor via the supervisordevice 110 in response to which, the content delivery network 100 canreceive a response from the user-supervisor either accepting arerefusing the recommendation. If the recommendation is refused, then theaggregation can be provided in its original form to the users in thecohort identified and block 850.

Alternatively, if the recommendation is accepted, then the aggregationcan be updated and the process 800 can return to block 854 and combinedaggregation data can be generated for the updated aggregation and theprocess 800 can then proceed as outlined above.

In some embodiments, the recommendation can be provided in the form ofan alert. The alert can be generated by the central server 102 and/orthe privacy server 108 and can be provided to at least one of the userdevices 106 and/or the supervisor device 110. In some embodiments, analert, containing information relevant to the recommended alteration canbe provided to the user-supervisor along with a prompt to indicatewhether the recommended alteration is accepted or refused.

In some embodiments, any information disclosed herein as provided to oneor several user devices 106 and/or supervisor devices 110 can beprovided in a form of an alert. In some embodiments, for example, theproviding of this alert can include the identification of one or severaluser device 106 and/or user accounts associated with the user. Afterthese one or several user devices 106 and/or user accounts have beenidentified, the providing of this alert can include determining a uselocation of the user based on determining if the user is actively usingone of the identified user devices 106 and/or accounts. In someembodiments, the use location may correspond to a physical location ofthe device 106, 110 being actively used, and in some embodiments, theuse location can comprise the user account and/or user device which thecreator of the thread is currently using.

If the user is actively using one of the user devices and/or accounts,the alert can be provided to the user via that user device 106 and/oraccount that is actively being used. If the user is not actively using auser device 106 and/or account, a default device, such as a smart phoneor tablet, can be identified and the alert can be provided to thisdefault device. In some embodiments, the alert can include code todirect the default device to provide an indicator of the received alertsuch as, for example, an aural, tactile, or visual indicator of receiptof the alert.

With reference now to FIG. 9, a flowchart illustrating one embodiment ofa process 900 for generating a customized aggregation matched to asub-cohort to maximize data transmission rates through the contentdistribution network 100 is shown. In some embodiments, datatransmission times, as measured from the transmission of a first datapacket of an aggregation until the transmission of a last data packet ofan aggregation, can be minimized by customizing the contents of theaggregation according to one or several aggregation attributes orattributes of the data packets in the aggregation. In some embodiments,such customization of the aggregation can increase data acceptancerates, and thereby increase transmission rates.

The process 900 can be performed by the content distribution network 100and/or one or several components of the content distribution network100. The process 900 begins at block 902, wherein aggregationinformation is received. In some embodiments, the aggregationinformation can identify one or several data packets, including one orseveral delivery data packets and/or one or several assessment datapackets, for intended delivery to one or several users, which users canbe, for example, one or several students, via, for example, one orseveral user devices 106. The aggregation information can be generatedby the content delivery network 100 or can be received from auser-supervisor such as a teacher, and instructor, or any individual,group, or entity responsible for providing content to one or severalother users as discussed above.

After the aggregation information has been received, the process 900proceeds to block 904 wherein the one or several data packets identifiedby the aggregation information are retrieved and/or received. In someembodiments, these one or several data packets can be retrieved orreceived from one of the databases such as the content library database303 by for example, the central processor 102 of the content deliverynetwork 100.

After the data packets have been retrieved, the process 900 proceeds toblock 906 wherein data packet data, which can include, for example, datapacket metadata and/or data packet user data is retrieved as discussedabove with respect to blocks 846, 848 of FIG. 8. After the data packetdata has been retrieved, the process 900 proceeds to block 908 wherein arecipient cohort is identified. In some embodiments, the recipientcohort is the group of users designated to receive the aggregation via aplurality of user devices 106. The recipient cohort can be determinedbased on information received from the user-supervisor and/or based onuser information stored in, for example, the user profile database 301.In one embodiment, for example, the recipient cohort can correspond tousers participating in a group such as, for example, the class, aprogram, the training group, or the like. In some embodiments, therecipient cohort can be identified by determining the group forreceiving the aggregation and then determining the users in that group.In one embodiment, for example, the user-supervisor can designate aclass for receiving the aggregation and the users in that class can beidentified based on, for example, enrollment information stored in theuser profile database 301.

After the recipient cohort has been identified, the process 900 proceedsto block 910, wherein recipient cohort user data is received orretrieved. In some embodiments, this recipient cohort user data can bereceived or retrieved from the user profile database 301. Thisinformation can identify one or several attributes of the user and/orcan identify one or several user actions. These attributes can include,for example, the data acceptance rate, a skill level, a learning style,or the like. The one or several user actions can include, for example,information identifying one or several data packets received by theuser, information identifying one or several responses provided by theuser including identifying one or several desired responses or undesiredresponses provided by the user, amount of time spent interacting withthe content distribution network 100, amount of time spent receivingand/or responding to data packets, or the like. In some embodiments,this can include receiving, and/or retrieval of one or several dataacceptance curves associated with one or several of the users in therecipient cohort.

After the recipient cohort user data has been retrieved, the process 900proceeds to block 912, wherein one or several sub-cohorts are createdby, for example, dividing the recipient cohort into smaller groups. Insome embodiments, the one or several cohorts comprise one or severalsub-sets of the users in the recipient cohort. In some embodiments,further matching of the aggregation to user attributes can be achievedby the formation of these one or several sub-cohorts.

These one or several sub-cohorts can be created in in a variety ofdifferent ways. In one exemplary embodiment, for example, one or severalsub-cohorts can be created by receiving information from theuser-supervisor specifying a desired number of sub-cohorts and one orseveral desired attributes for use in forming these cohorts. In such anembodiment, and if several desired attributes are desired for use increating the sub-cohorts, the user-supervisor can further provideinformation specifying a priority and/or a relative ranking among theattributes for use in creating the sub-cohorts. After this informationhas been received by the content distribution network 100, the one orseveral users in the recipient cohort can be relatively ranked withrespect to each other vis-à-vis the one or several identified attributesand the users in the recipient cohort can then be divided intosub-cohorts based on the relative ranking by, for example, creating thespecified number of sub-cohorts of an equal size.

Alternatively, having received the desired number of sub-cohorts in theone or several attributes for creating the sub-cohorts, the contentdistribution network can evaluate the users of the recipient group basedon those one or several attributes and identify sub-cohorts optimizedsuch that members of the sub-cohorts have the maximum amount ofsimilarity of attributes with other members of the same sub-cohort.

After the sub-cohorts have been created, the process 900 proceeds toblock 914 wherein combined aggregation data is generated. In someembodiments, the combined aggregation data can be generated from one orboth of the content data received in block 906. This combinedaggregation data can characterize an attribute of the aggregation as awhole such as, for example, the difficulty of the aggregation as awhole, the differentiation of the aggregation as a whole, theanticipated time required for the aggregation as a whole, or the like.In some embodiments, this combined aggregation data can be generated byidentifying and combining like attributes from the data packets in theaggregation according to known techniques.

After the combined aggregation data has been generated, the process 900proceeds to block 916 wherein sub-cohort data is generated. In someembodiments, combined cohort data can be generated from user data ofusers belonging to a sub-cohort. In some embodiments, sub-cohort datacan be generated for each of the sub-cohorts. This sub-cohort data cancharacterize an attribute of the sub-cohort as a whole such as, forexample, the data acceptance rate, the skill level, lapsed timeincluding the time already spent by users and the recipient cohortreceiving and/or responding to data packets, or the like. In someembodiments, this sub-cohort data can be generated by identifying andcombining the like attributes from users in the sub-cohort according toknown techniques.

After the sub-cohort data has been generated, the process 900 proceedsto block 919 wherein the combined aggregation data is compared to thesub-cohort data for some or all of the sub-cohorts. In some embodiments,this comparison is separately performed for some or all of thesub-cohorts. In some embodiments, this comparison can be performed by acomponent of the content distribution network 100 such as the centralserver 102. This comparison can determine whether one or severalattributes of the aggregation correspond, or more specificallysatisfactorily correspond to one or several attributes of each of thesub-cohorts. In some embodiments, the content distribution network 100can determine a difference between one or several attributes indicatedin the combined aggregation data and in the sub-cohort data for each ofthe sub-cohorts, and can compare this difference to a threshold value.

In some embodiments, the comparison of the combined aggregation data andthe sub-cohort data can include determining whether the difficulty levelof the aggregation corresponds the skill level of the sub-cohort, if thedata acceptance rate of the aggregation as adapted to correspond to oneor several attributes of the sub-cohort is acceptable, determiningwhether the differentiation level of the aggregation is satisfactory, orthe like.

In some embodiments, for example, the data acceptance rate for theaggregation can be adjusted to correspond to attributes of the users inthe sub-cohort. Specifically, one or several sub-cohort parametersrelating to users in the sub-cohort can be used to filter data used ingenerating the aggregation data acceptance rate such that the data usedin generating the aggregation data acceptance rate approximatesattributes of the sub-cohort as a whole or of individual users in thesub-cohort. In some embodiments, for example, this can include filteringuser data used in generating the data acceptance rate for theaggregation such that user data for users having similar skill levels tousers in the sub-cohort is included. By doing this, user data acceptancerates of users in a sub-cohort corresponds to those in the user dataused in generating the data acceptance rate.

After the combined aggregation data and the sub-cohort data have beencompared, the process 900 proceeds to decision block 918 wherein it isdetermined, for some or all of the sub-cohorts, whether the aggregationis compatible with the sub-cohorts. In some embodiments, thisdetermination includes selecting one of the sub-cohorts, receiving theresults of the comparison in block 919, and determining whether the oneor several compared attributes of the combined aggregation data and thecombined sub-cohort data of the selected sub-cohort satisfactorilymatch. If there is a satisfactory match, then the process 900 proceedsto block 920 wherein the aggregation is recommended for use to a usersuch as, for example, the user-supervisor via the supervisor device 110.

After the aggregation has been recommended, the process 900 proceeds toblock 922, wherein the aggregation is provided to the users of matchingsub-cohort. In some embodiments, the aggregation can be provided to theusers of the matching sub-cohort via one or several user devices 106.

Returning again to decision block 918, if it is determined that there isnot a satisfactory match between the sub-cohort data for one of thesub-cohorts and the combined aggregation data, then the process 900proceeds to block 924 wherein an alteration to the aggregation isrecommended. In some embodiments, this recommended alteration is generalto all sub-cohorts, and in some embodiments, this alteration is specificto the sub-cohort having sub-cohort data that did not adequately matchthe combined aggregation data. Thus, in some embodiments, therecommendation for alteration can be a request for the generation of anaggregation specifically tailored to the user attributes of one of thesub-cohorts. This can, in some embodiments, result in some or all of thesub-cohorts receiving a uniquely tailored aggregation.

In some embodiments, this recommendation can include identification ofone or several data packets in the aggregation for removal and/or theidentification of one or several data packets for inclusion in theaggregation. These data packets can be identified for removal and/or forinclusion based on one or several attributes of these data packets. Inone embodiment for example in which the difficulty of the aggregation istoo high, one or several data packets can be identified that meet theparameters of the aggregation but that are less difficult and these datapackets can be recommended for inclusion in the aggregation. Similarly,data packets having other attributes can be identified and recommendedfor removal from or inclusion in the aggregation. In some embodiments,the recommendation of the alteration can be provided to theuser-supervisor via the supervisor device 110.

Specifically, in one embodiment, one or several data packets can beidentified for removal by identifying the content items in theaggregation. After the content items have been identified a contributionvalue can be determined, which contribution value characterizes thedegree to which one or several attributes contribute to the failure ofthe aggregation to meet the threshold. In some embodiments, this caninclude determining a difference between the attribute for each of thedata packets and the sub-cohort aggregate, and then comparing thisdifference to the threshold value. In some embodiments, the data packetscan be ranked according to the degree to which they detrimentallycontribute to the failure of the aggregation to meet the threshold, andone or several of the worst ranked data packets can be selected forremoval from the aggregation.

In some embodiments, one or several data packets can be selected forinclusion in the aggregation by retrieving the aggregation database oraggregation database information. In some embodiments, the aggregationdatabase or aggregation database information identifies data packetsthat are includable in the aggregation. After the aggregation databasehas been retrieved, a content item from the aggregation database can beselected for evaluation, which content item can, in some embodiments,not be presently included in the aggregation. In some embodiments, oneor several differences can be determined between one or severalattributes of the content item and the sub-cohort data, and thesedifferences can be compared to the threshold value. This selection andcomparison of content items can be repeated until all data packets inthe aggregation database, or a desired number of data packets in theaggregation database have been evaluated. These data packets can then berank ordered according to the degree to which their inclusion in theaggregation would contribute to the meeting of threshold values.

In some embodiments data packets can then be selected for inclusion bycomparing the difference of potential data packets for inclusion to thedifference of potential data packets for removal. In some embodiments,this comparison can be performed according to the ranked order of boththe data packets for potential inclusion and the data packets forpotential removal. In some embodiments, a data packet can be designatedfor removal if a potential data packet for inclusion would morepositively contribute to the meeting of the threshold. Thisidentification of data packets for removal and inclusion can continueuntil an altered aggregation is created that would meet the threshold oruntil there are no additional data packets in the aggregation databasethat more positively contribute to meeting of the threshold than datapackets already included in the aggregation.

After the alteration has been recommended, the process 900 proceeds todecision block 926 wherein it is determined if the recommendation isaccepted. In some embodiments, and in response to the alterationrecommendation, the content delivery network 100 can receive a responsefrom the user-supervisor via the supervisor device 110 either acceptingare refusing the recommendation. If the alteration recommendation is notaccepted, then the process 900 advances to block 922 and proceeds asoutlined above. If the alteration recommendation is accepted, then theaggregation can be altered per the recommendation as indicated in block928 and the process 900 can return to block 904 and proceed as outlinedabove. This updated aggregation can be provided to the user via a userdevice 106 if after repeating steps 904-919, it is determined that thecombined aggregation data of the updated aggregation corresponds to thesub-cohort data.

With reference now to FIG. 10, a flowchart illustrating one embodimentof a process 1000 for generating an aggregation database is shown. Thiscan involve the receipt of a group of data packets and the selection ofa subgroup of those data packets for inclusion in the aggregationdatabase. In some embodiments, data packets are included in theaggregation database based on one or several attributes of the datapackets such as, for example, difficulty, differentiation, and arandomness factor. In some embodiments, the aggregation database can becustomized for an aggregation based on one or several desired attributesof the aggregation, thus, when the aggregation database is created, theaggregation can contain a set of data packets that are suitable for usein the aggregation.

The process 1000 can be performed by the content distribution network100 and/or one or several components thereof. The process 1000 begins atblock 1002, wherein a group, also referred to herein as a set, of datapackets is received. In some embodiments, the set of questions can bereceived from the content library database 303. After the data packetshave been retrieved, the process 1000 proceeds to block 1004, whereinthe data packet user data is retrieved. In some embodiments, the datapacket user data can be retrieved simultaneous with the retrieval of thedata packets, and can be retrieved from the content library database303.

After the data packet user data has been retrieved, the process 1000proceeds to block 1006, wherein a data packet attribute is determinedfor some or all of the retrieved data packets. This data packetattribute can be, for example, a difficulty, skill level,differentiation level, randomness level, a rate of data acceptance, orthe like. In some embodiments, these data packet attributes can bedetermined, for example, from one or more of one or several dataacceptance curves 600 for a data packet, and/or from one or several datapacket curves 700.

After the data packet attribute has been determined, the process 1000proceeds to block 1008, wherein one or several quality thresholds areretrieved. In some embodiments, the quality threshold can be based ondesired attributes for an aggregation such as can be specified asdiscussed above via a GUI on the supervisor device 110. These qualitythresholds can be stored in one of the databases such as, for example,content library database 303. After the quality threshold is retrieved,the process 1000 proceeds to block 1010, wherein the quality thresholdsare compared with the data packet attributes determined in block 1006.

After the quality thresholds are compared to the data packet attributes,the process 1000 proceeds to decision block 1012, wherein it isdetermined if the randomness attribute of the data packet exceeds thequality threshold for randomness. In some embodiments, this comparisoncan determine whether the data packet is too likely to receive a desiredresponse based on randomness, and particularly based on guessing. If therandomness attribute exceeds the quality attribute for randomness, thenthe process 1000 proceeds to block 1014 and the data packet is excludedfrom the aggregation database and an indicator of excessive randomnessis provided to the user-supervisor and/or is stored in the contentlibrary database 303.

If it is determined that the randomness attribute does not exceed thequality attribute for randomness, then the process 1000 proceeds todecision block 1016, wherein it is determined if the differentiationattribute exceeds the quality threshold for differentiation. In someembodiments, this can determine whether the data packet sufficientlydifferentiates between skill levels. If the differentiation level doesnot exceed the threshold for differentiation, the data packet hasinsufficient differentiation and the process 1000 proceeds to block1018, and the data packet is excluded from the aggregation database andan indicator of insufficient differentiation is provided to theuser-supervisor and/or is stored in the content library database 303.

If it is determined that the differentiation attribute exceeds thequality attribute for differentiation and that the data packet hassufficient differentiation, then the process 1000 proceeds decisionblock 1020, wherein it is determine if the difficulty attribute of thedata packet is acceptable, or inacceptable as either being too high ortoo low vis-à-vis the quality threshold for difficulty. If it isdetermined that the difficulty level of the data packet is too high,then the process 1000 proceeds to block 1022 and the data packet isexcluded from the aggregation database and an indicator of excessivedifficulty is provided to the user-supervisor and/or is stored in thecontent library database 303. Similarly, if it is determined that thedifficulty of the data packet is too low, then the process 1000 proceedsto block 1024 and the data packet is excluded from the aggregationdatabase and an indicator of inadequate difficulty is provided to theuser-supervisor and/or is stored in the content library database 303.

If it is determined that the difficulty of the data packet matches thequality threshold for difficulty, then the process 1000 proceeds toblock 1026 and an indicator of acceptability is associated with the datapacket, for example, in the content library database 303. The process1000 then proceeds to block 1028, wherein the data packet is stored inthe aggregation database in, for example, the content library database.In some embodiments, process 1000 can be repeated until all of the datapackets retrieved in block 1002 have been evaluated and either includedin, or excluded from the aggregation database. In some embodiments, thedata packets in the aggregation database can then be selected accordingto one or both of processes 800 and 900 in creating the aggregation.

A number of variations and modifications of the disclosed embodimentscan also be used. Specific details are given in the above description toprovide a thorough understanding of the embodiments. However, it isunderstood that the embodiments may be practiced without these specificdetails. For example, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the embodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long term, short term, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A system for increasing data transmission ratesthrough a content distribution network by generating a customizedaggregation comprising data packets selected to maximize a dataacceptance rate, the system comprising: a memory comprising: a contentlibrary database comprising a plurality of data packets, wherein theplurality of data packets comprise a plurality of delivery data packetsand a plurality of assessment data packets; and a user profile database,wherein the user profile database includes information identifying acohort of users, and wherein the user profile database includesinformation identifying plurality of at least one attribute of each ofthe users in the cohort of users; a server configured to: receiveaggregation information identifying a plurality of delivery data packetsand a plurality of assessment data packets; receive data packet datafrom the content library database; identify a recipient cohort, whereinthe recipient cohort comprises the group of users designated to receivethe aggregation via a plurality of user devices; generate a plurality ofsub-cohorts by dividing the cohort into smaller groups, wherein theusers in each of the sub-cohorts share a common attribute; generatecombined aggregation data characterizing the aggregation as a whole;generate an updated aggregation by removing at least one data packetfrom the aggregation; and provide the updated aggregation to the usersin the sub-cohort.
 2. The system of claim 1, wherein the data packetdata comprises data packet user data and data packet metadata.
 3. Thesystem of claim 1, further comprising a plurality of user devicesconnected to the server via a communication network.
 4. The system ofclaim 1, wherein the server is further configured to generate sub-cohortdata, wherein the sub-cohort data can be generated for each of thesub-cohorts from data of users in that sub-cohort.
 5. The system ofclaim 4, wherein the combined aggregation data identifies a dataacceptance rate.
 6. The system of claim 5, wherein the server is furtherconfigured to identify the at least one data packet for removal bydetermining a difference between the aggregation data to the sub-cohortdata, and comparing the difference to a threshold.
 7. The system ofclaim 6, wherein generating an updated aggregation further comprisesincluding at least one new data packet in the aggregation.
 8. The systemof claim 7, wherein the at least one new data packet is selected forinclusion in the aggregation based on a degree of in minimizing thedifference between the aggregation data and the sub-cohort data.
 9. Thesystem of claim 7, wherein the at least one new data packet is selectedfor inclusion in the aggregation based on a degree of optimizing thedifference between the aggregation data and the sub-cohort data.
 10. Thesystem of claim 4, wherein the combined aggregation data identifies adifficulty.
 11. A method for increasing data transmission rates througha content distribution network by generating a customized aggregationcomprising data packets selected to maximize a data acceptance rate, themethod comprising: receiving aggregation information identifying aplurality of delivery data packets and a plurality of assessment datapackets, wherein the plurality of delivery data packets and theplurality of assessment data packets are stored in a content librarydatabase; receiving with a server data packet data from the contentlibrary database, wherein the data packet data identifies one or severalattributes of the delivery data packets and the assessment data packetsin the aggregation information; identifying with the server a recipientcohort, wherein the recipient cohort comprises the group of usersdesignated to receive the aggregation via a plurality of user devices;generating with the server a plurality of sub-cohorts by dividing thecohort into smaller groups, wherein the users in each of the sub-cohortsshare a common attribute; generating with the server combinedaggregation data characterizing the aggregation as a whole; generatingwith the server an updated aggregation by removing at least one datapacket from the aggregation, wherein the aggregation is updated toincrease a data acceptance rate; and providing with the server theupdated aggregation to the users in the sub-cohort via a plurality ofuser devices connected to the server by a communication network.
 12. Themethod of claim 11, wherein the data packet data comprises data packetuser data and data packet metadata.
 13. The method of claim 11, furthercomprising generating sub-cohort data, wherein the sub-cohort data canbe generated for each of the sub-cohorts from data of users in thatsub-cohort.
 14. The method of claim 13, wherein the combined aggregationdata identifies a data acceptance rate.
 15. The method of claim 14,further comprising identifying the at least one data packet for removalby determining a difference between the aggregation data to thesub-cohort data, and comparing the difference to a threshold.
 16. Themethod of claim 15, wherein generating an updated aggregation furthercomprises including at least one new data packet in the aggregation. 17.The method of claim 16, wherein the at least one new data packet isselected for inclusion in the aggregation based on a degree of inminimizing the difference between the aggregation data and thesub-cohort data.
 18. The method of claim 16, wherein the at least onenew data packet is selected for inclusion in the aggregation based on adegree of optimizing the difference between the aggregation data and thesub-cohort data.
 19. The method of claim 13, wherein the combinedaggregation data identifies a difficulty.